2024-12-15 14:40:03 +01:00
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import sys
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from pathlib import Path
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from unittest.mock import Mock, patch
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import pytest
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2025-06-10 22:00:28 +02:00
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from loguru import logger
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2024-12-15 14:40:03 +01:00
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from akkudoktoreos.core.ems import get_ems
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from akkudoktoreos.prediction.prediction import get_prediction
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from akkudoktoreos.prediction.pvforecastakkudoktor import (
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AkkudoktorForecastHorizon,
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AkkudoktorForecastMeta,
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AkkudoktorForecastValue,
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PVForecastAkkudoktor,
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PVForecastAkkudoktorDataRecord,
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)
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from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime, to_duration
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DIR_TESTDATA = Path(__file__).absolute().parent.joinpath("testdata")
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FILE_TESTDATA_PV_FORECAST_INPUT_1 = DIR_TESTDATA.joinpath("pv_forecast_input_1.json")
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2025-05-30 21:55:45 +02:00
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FILE_TESTDATA_PV_FORECAST_INPUT_SINGLE_PLANE = DIR_TESTDATA.joinpath(
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"pv_forecast_input_single_plane.json"
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)
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2024-12-15 14:40:03 +01:00
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FILE_TESTDATA_PV_FORECAST_RESULT_1 = DIR_TESTDATA.joinpath("pv_forecast_result_1.txt")
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@pytest.fixture
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2024-12-30 13:41:39 +01:00
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def sample_settings(config_eos):
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2024-12-15 14:40:03 +01:00
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"""Fixture that adds settings data to the global config."""
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settings = {
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2025-01-20 22:58:59 +01:00
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"general": {
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"latitude": 52.52,
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"longitude": 13.405,
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},
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2025-01-12 05:19:37 +01:00
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"prediction": {
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2025-01-18 14:26:34 +01:00
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"hours": 48,
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"historic_hours": 24,
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2025-01-12 05:19:37 +01:00
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},
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"pvforecast": {
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2025-01-18 14:26:34 +01:00
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"provider": "PVForecastAkkudoktor",
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2025-01-19 18:12:50 +01:00
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"planes": [
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{
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"peakpower": 5.0,
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fix: azimuth setting of pvforecastakkudoktor provider (#567)
EOS now enforces the general azimuth definition as e.g. defined in ISO 19111:
north=0, east=90, south=180, west=270. This is the convention that is and was
in the EOS documentation.
As the PV forecast of akkudoktor.net follows a different convention
(north=+-180, east=-90, south=0, west=90) the values from EOS are now converted
before the request is sent to akkudoktor.net.
BREAKING CHANGE: Azimuth configurations that followed the PVForecastAkkudoktor convention
(north=+-180, east=-90, south=0, west=90) must be converted to the general azimuth definition:
north=0, east=90, south=180, west=270.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-05-28 20:42:43 +02:00
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"surface_azimuth": 170,
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2025-01-19 18:12:50 +01:00
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"surface_tilt": 7,
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"userhorizon": [20, 27, 22, 20],
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"inverter_paco": 10000,
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},
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{
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"peakpower": 4.8,
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fix: azimuth setting of pvforecastakkudoktor provider (#567)
EOS now enforces the general azimuth definition as e.g. defined in ISO 19111:
north=0, east=90, south=180, west=270. This is the convention that is and was
in the EOS documentation.
As the PV forecast of akkudoktor.net follows a different convention
(north=+-180, east=-90, south=0, west=90) the values from EOS are now converted
before the request is sent to akkudoktor.net.
BREAKING CHANGE: Azimuth configurations that followed the PVForecastAkkudoktor convention
(north=+-180, east=-90, south=0, west=90) must be converted to the general azimuth definition:
north=0, east=90, south=180, west=270.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-05-28 20:42:43 +02:00
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"surface_azimuth": 90,
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2025-01-19 18:12:50 +01:00
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"surface_tilt": 7,
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"userhorizon": [30, 30, 30, 50],
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"inverter_paco": 10000,
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},
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{
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"peakpower": 1.4,
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fix: azimuth setting of pvforecastakkudoktor provider (#567)
EOS now enforces the general azimuth definition as e.g. defined in ISO 19111:
north=0, east=90, south=180, west=270. This is the convention that is and was
in the EOS documentation.
As the PV forecast of akkudoktor.net follows a different convention
(north=+-180, east=-90, south=0, west=90) the values from EOS are now converted
before the request is sent to akkudoktor.net.
BREAKING CHANGE: Azimuth configurations that followed the PVForecastAkkudoktor convention
(north=+-180, east=-90, south=0, west=90) must be converted to the general azimuth definition:
north=0, east=90, south=180, west=270.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-05-28 20:42:43 +02:00
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"surface_azimuth": 140,
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2025-01-19 18:12:50 +01:00
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"surface_tilt": 60,
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"userhorizon": [60, 30, 0, 30],
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"inverter_paco": 2000,
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},
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{
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"peakpower": 1.6,
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fix: azimuth setting of pvforecastakkudoktor provider (#567)
EOS now enforces the general azimuth definition as e.g. defined in ISO 19111:
north=0, east=90, south=180, west=270. This is the convention that is and was
in the EOS documentation.
As the PV forecast of akkudoktor.net follows a different convention
(north=+-180, east=-90, south=0, west=90) the values from EOS are now converted
before the request is sent to akkudoktor.net.
BREAKING CHANGE: Azimuth configurations that followed the PVForecastAkkudoktor convention
(north=+-180, east=-90, south=0, west=90) must be converted to the general azimuth definition:
north=0, east=90, south=180, west=270.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-05-28 20:42:43 +02:00
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"surface_azimuth": 185,
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2025-01-19 18:12:50 +01:00
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"surface_tilt": 45,
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"userhorizon": [45, 25, 30, 60],
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"inverter_paco": 1400,
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},
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],
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2025-01-12 05:19:37 +01:00
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},
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2024-12-15 14:40:03 +01:00
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}
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# Merge settings to config
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config_eos.merge_settings_from_dict(settings)
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2025-01-18 14:26:34 +01:00
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assert config_eos.pvforecast.provider == "PVForecastAkkudoktor"
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2024-12-15 14:40:03 +01:00
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return config_eos
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@pytest.fixture
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def sample_forecast_data():
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"""Fixture that returns sample forecast data converted to pydantic model."""
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2025-02-12 21:35:51 +01:00
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with FILE_TESTDATA_PV_FORECAST_INPUT_1.open("r", encoding="utf-8", newline=None) as f_in:
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2024-12-15 14:40:03 +01:00
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input_data = f_in.read()
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return PVForecastAkkudoktor._validate_data(input_data)
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@pytest.fixture
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def sample_forecast_data_raw():
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"""Fixture that returns raw sample forecast data."""
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2025-02-12 21:35:51 +01:00
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with FILE_TESTDATA_PV_FORECAST_INPUT_1.open("r", encoding="utf-8", newline=None) as f_in:
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2024-12-15 14:40:03 +01:00
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input_data = f_in.read()
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return input_data
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2025-05-30 21:55:45 +02:00
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@pytest.fixture
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def sample_forecast_data_single_plane_raw():
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"""Fixture that returns raw sample forecast data."""
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with FILE_TESTDATA_PV_FORECAST_INPUT_SINGLE_PLANE.open(
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"r", encoding="utf-8", newline=None
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) as f_in:
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input_data = f_in.read()
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return input_data
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2024-12-15 14:40:03 +01:00
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@pytest.fixture
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def sample_forecast_report():
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"""Fixture that returns sample forecast data report."""
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2025-02-12 21:35:51 +01:00
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with FILE_TESTDATA_PV_FORECAST_RESULT_1.open("r", encoding="utf-8", newline=None) as f_res:
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2024-12-15 14:40:03 +01:00
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input_data = f_res.read()
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return input_data
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@pytest.fixture
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def sample_forecast_start(sample_forecast_data):
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"""Fixture that returns the start date of the sample forecast data."""
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forecast_start = to_datetime(sample_forecast_data.values[0][0].datetime)
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expected_datetime = to_datetime("2024-10-06T00:00:00.000+02:00")
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assert compare_datetimes(to_datetime(forecast_start), expected_datetime).equal
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timezone_name = sample_forecast_data.meta.timezone
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assert timezone_name == "Europe/Berlin"
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return forecast_start
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@pytest.fixture
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def provider():
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"""Fixture that returns the PVForecastAkkudoktor instance from the prediction."""
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prediction = get_prediction()
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provider = prediction.provider_by_id("PVForecastAkkudoktor")
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assert isinstance(provider, PVForecastAkkudoktor)
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return provider
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@pytest.fixture
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def provider_empty_instance():
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"""Fixture that returns an empty instance of PVForecast."""
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empty_instance = PVForecastAkkudoktor()
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empty_instance.clear()
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assert len(empty_instance) == 0
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return empty_instance
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# Sample data for testing
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sample_horizon = AkkudoktorForecastHorizon(altitude=30, azimuthFrom=90, azimuthTo=180)
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sample_meta = AkkudoktorForecastMeta(
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lat=52.52,
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lon=13.405,
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power=[5000],
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azimuth=[180],
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tilt=[30],
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timezone="Europe/Berlin",
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albedo=0.25,
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past_days=5,
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inverterEfficiency=0.8,
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powerInverter=[10000],
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cellCoEff=-0.36,
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range=True,
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horizont=[[sample_horizon]],
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horizontString=["sample_horizon"],
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)
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sample_value = AkkudoktorForecastValue(
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datetime="2024-11-09T12:00:00",
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dcPower=500.0,
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power=480.0,
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sunTilt=30.0,
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sunAzimuth=180.0,
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temperature=15.0,
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relativehumidity_2m=50.0,
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windspeed_10m=10.0,
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)
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sample_config_data = {
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2025-01-20 22:58:59 +01:00
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"general": {
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"latitude": 52.52,
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"longitude": 13.405,
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},
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2025-01-12 05:19:37 +01:00
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"prediction": {
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2025-01-18 14:26:34 +01:00
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"hours": 48,
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"historic_hours": 24,
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2025-01-12 05:19:37 +01:00
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},
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"pvforecast": {
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2025-01-18 14:26:34 +01:00
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"provider": "PVForecastAkkudoktor",
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2025-01-19 21:47:21 +01:00
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"planes": [
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{
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"peakpower": 5.0,
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"surface_azimuth": 180,
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"surface_tilt": 30,
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"inverter_paco": 10000,
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}
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],
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2025-01-12 05:19:37 +01:00
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},
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2024-12-15 14:40:03 +01:00
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}
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# Tests for AkkudoktorForecastHorizon
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def test_akkudoktor_forecast_horizon():
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horizon = AkkudoktorForecastHorizon(altitude=30, azimuthFrom=90, azimuthTo=180)
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assert horizon.altitude == 30
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assert horizon.azimuthFrom == 90
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assert horizon.azimuthTo == 180
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# Tests for AkkudoktorForecastMeta
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def test_akkudoktor_forecast_meta():
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meta = sample_meta
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assert meta.lat == 52.52
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2025-01-02 18:34:55 +01:00
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assert meta.lon == 13.405
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2024-12-15 14:40:03 +01:00
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assert meta.power == [5000]
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assert meta.tilt == [30]
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assert meta.timezone == "Europe/Berlin"
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# Tests for AkkudoktorForecastValue
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def test_akkudoktor_forecast_value():
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value = sample_value
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assert value.dcPower == 500.0
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assert value.power == 480.0
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assert value.temperature == 15.0
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assert value.windspeed_10m == 10.0
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# Tests for PVForecastAkkudoktorDataRecord
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def test_pvforecast_akkudoktor_data_record():
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record = PVForecastAkkudoktorDataRecord(
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pvforecastakkudoktor_ac_power_measured=1000.0,
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pvforecastakkudoktor_wind_speed_10m=10.0,
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pvforecastakkudoktor_temp_air=15.0,
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)
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assert record.pvforecastakkudoktor_ac_power_measured == 1000.0
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assert record.pvforecastakkudoktor_wind_speed_10m == 10.0
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assert record.pvforecastakkudoktor_temp_air == 15.0
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assert (
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record.pvforecastakkudoktor_ac_power_any == 1000.0
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) # Assuming AC power measured is preferred
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def test_pvforecast_akkudoktor_validate_data(provider_empty_instance, sample_forecast_data_raw):
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"""Test validation of PV forecast data on sample data."""
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2025-01-26 22:42:54 +01:00
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logger.info("The following errors are intentional and part of the test.")
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2024-12-15 14:40:03 +01:00
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with pytest.raises(
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ValueError,
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match="Field: meta\nError: Field required\nType: missing\nField: values\nError: Field required\nType: missing\n",
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):
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ret = provider_empty_instance._validate_data("{}")
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data = provider_empty_instance._validate_data(sample_forecast_data_raw)
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# everything worked
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2025-05-30 21:55:45 +02:00
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def test_pvforecast_akkudoktor_validate_data_single_plane(
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provider_empty_instance, sample_forecast_data_single_plane_raw
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):
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"""Test validation of PV forecast data on sample data with a single plane."""
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logger.info("The following errors are intentional and part of the test.")
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with pytest.raises(
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ValueError,
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match="Field: meta\nError: Field required\nType: missing\nField: values\nError: Field required\nType: missing\n",
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):
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ret = provider_empty_instance._validate_data("{}")
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data = provider_empty_instance._validate_data(sample_forecast_data_single_plane_raw)
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|
# everything worked
|
|
|
|
|
|
|
|
|
|
|
2024-12-15 14:40:03 +01:00
|
|
|
@patch("requests.get")
|
|
|
|
|
def test_pvforecast_akkudoktor_update_with_sample_forecast(
|
|
|
|
|
mock_get, sample_settings, sample_forecast_data_raw, sample_forecast_start, provider
|
|
|
|
|
):
|
|
|
|
|
"""Test data processing using sample forecast data."""
|
|
|
|
|
# Mock response object
|
|
|
|
|
mock_response = Mock()
|
|
|
|
|
mock_response.status_code = 200
|
|
|
|
|
mock_response.content = sample_forecast_data_raw
|
|
|
|
|
mock_get.return_value = mock_response
|
|
|
|
|
|
|
|
|
|
# Test that update properly inserts data records
|
2024-12-30 13:41:39 +01:00
|
|
|
ems_eos = get_ems()
|
2024-12-15 14:40:03 +01:00
|
|
|
ems_eos.set_start_datetime(sample_forecast_start)
|
|
|
|
|
provider.update_data(force_enable=True, force_update=True)
|
fix: automatic optimization (#596)
This fix implements the long term goal to have the EOS server run optimization (or
energy management) on regular intervals automatically. Thus clients can request
the current energy management plan at any time and it is updated on regular
intervals without interaction by the client.
This fix started out to "only" make automatic optimization (or energy management)
runs working. It turned out there are several endpoints that in some way
update predictions or run the optimization. To lock against such concurrent attempts
the code had to be refactored to allow control of execution. During refactoring it
became clear that some classes and files are named without a proper reference
to their usage. Thus not only refactoring but also renaming became necessary.
The names are still not the best, but I hope they are more intuitive.
The fix includes several bug fixes that are not directly related to the automatic optimization
but are necessary to keep EOS running properly to do the automatic optimization and
to test and document the changes.
This is a breaking change as the configuration structure changed once again and
the server API was also enhanced and streamlined. The server API that is used by
Andreas and Jörg in their videos has not changed.
* fix: automatic optimization
Allow optimization to automatically run on configured intervals gathering all
optimization parameters from configuration and predictions. The automatic run
can be configured to only run prediction updates skipping the optimization.
Extend documentaion to also cover automatic optimization. Lock automatic runs
against runs initiated by the /optimize or other endpoints. Provide new
endpoints to retrieve the energy management plan and the genetic solution
of the latest automatic optimization run. Offload energy management to thread
pool executor to keep the app more responsive during the CPU heavy optimization
run.
* fix: EOS servers recognize environment variables on startup
Force initialisation of EOS configuration on server startup to assure
all sources of EOS configuration are properly set up and read. Adapt
server tests and configuration tests to also test for environment
variable configuration.
* fix: Remove 0.0.0.0 to localhost translation under Windows
EOS imposed a 0.0.0.0 to localhost translation under Windows for
convenience. This caused some trouble in user configurations. Now, as the
default IP address configuration is 127.0.0.1, the user is responsible
for to set up the correct Windows compliant IP address.
* fix: allow names for hosts additional to IP addresses
* fix: access pydantic model fields by class
Access by instance is deprecated.
* fix: down sampling key_to_array
* fix: make cache clear endpoint clear all cache files
Make /v1/admin/cache/clear clear all cache files. Before it only cleared
expired cache files by default. Add new endpoint /v1/admin/clear-expired
to only clear expired cache files.
* fix: timezonefinder returns Europe/Paris instead of Europe/Berlin
timezonefinder 8.10 got more inaccurate for timezones in europe as there is
a common timezone. Use new package tzfpy instead which is still returning
Europe/Berlin if you are in Germany. tzfpy also claims to be faster than
timezonefinder.
* fix: provider settings configuration
Provider configuration used to be a union holding the settings for several
providers. Pydantic union handling does not always find the correct type
for a provider setting. This led to exceptions in specific configurations.
Now provider settings are explicit comfiguration items for each possible
provider. This is a breaking change as the configuration structure was
changed.
* fix: ClearOutside weather prediction irradiance calculation
Pvlib needs a pandas time index. Convert time index.
* fix: test config file priority
Do not use config_eos fixture as this fixture already creates a config file.
* fix: optimization sample request documentation
Provide all data in documentation of optimization sample request.
* fix: gitlint blocking pip dependency resolution
Replace gitlint by commitizen. Gitlint is not actively maintained anymore.
Gitlint dependencies blocked pip from dependency resolution.
* fix: sync pre-commit config to actual dependency requirements
.pre-commit-config.yaml was out of sync, also requirements-dev.txt.
* fix: missing babel in requirements.txt
Add babel to requirements.txt
* feat: setup default device configuration for automatic optimization
In case the parameters for automatic optimization are not fully defined a
default configuration is setup to allow the automatic energy management
run. The default configuration may help the user to correctly define
the device configuration.
* feat: allow configuration of genetic algorithm parameters
The genetic algorithm parameters for number of individuals, number of
generations, the seed and penalty function parameters are now avaliable
as configuration options.
* feat: allow configuration of home appliance time windows
The time windows a home appliance is allowed to run are now configurable
by the configuration (for /v1 API) and also by the home appliance parameters
(for the classic /optimize API). If there is no such configuration the
time window defaults to optimization hours, which was the standard before
the change. Documentation on how to configure time windows is added.
* feat: standardize mesaurement keys for battery/ ev SoC measurements
The standardized measurement keys to report battery SoC to the device
simulations can now be retrieved from the device configuration as a
read-only config option.
* feat: feed in tariff prediction
Add feed in tarif predictions needed for automatic optimization. The feed in
tariff can be retrieved as fixed feed in tarif or can be imported. Also add
tests for the different feed in tariff providers. Extend documentation to
cover the feed in tariff providers.
* feat: add energy management plan based on S2 standard instructions
EOS can generate an energy management plan as a list of simple instructions.
May be retrieved by the /v1/energy-management/plan endpoint. The instructions
loosely follow the S2 energy management standard.
* feat: make measurement keys configurable by EOS configuration.
The fixed measurement keys are replaced by configurable measurement keys.
* feat: make pendulum DateTime, Date, Duration types usable for pydantic models
Use pydantic_extra_types.pendulum_dt to get pydantic pendulum types. Types are
added to the datetimeutil utility. Remove custom made pendulum adaptations
from EOS pydantic module. Make EOS modules use the pydantic pendulum types
managed by the datetimeutil module instead of the core pendulum types.
* feat: Add Time, TimeWindow, TimeWindowSequence and to_time to datetimeutil.
The time windows are are added to support home appliance time window
configuration. All time classes are also pydantic models. Time is the base
class for time definition derived from pendulum.Time.
* feat: Extend DataRecord by configurable field like data.
Configurable field like data was added to support the configuration of
measurement records.
* feat: Add additional information to health information
Version information is added to the health endpoints of eos and eosDash.
The start time of the last optimization and the latest run time of the energy
management is added to the EOS health information.
* feat: add pydantic merge model tests
* feat: add plan tab to EOSdash
The plan tab displays the current energy management instructions.
* feat: add predictions tab to EOSdash
The predictions tab displays the current predictions.
* feat: add cache management to EOSdash admin tab
The admin tab is extended by a section for cache management. It allows to
clear the cache.
* feat: add about tab to EOSdash
The about tab resembles the former hello tab and provides extra information.
* feat: Adapt changelog and prepare for release management
Release management using commitizen is added. The changelog file is adapted and
teh changelog and a description for release management is added in the
documentation.
* feat(doc): Improve install and devlopment documentation
Provide a more concise installation description in Readme.md and add extra
installation page and development page to documentation.
* chore: Use memory cache for interpolation instead of dict in inverter
Decorate calculate_self_consumption() with @cachemethod_until_update to cache
results in memory during an energy management/ optimization run. Replacement
of dict type caching in inverter is now possible because all optimization
runs are properly locked and the memory cache CacheUntilUpdateStore is properly
cleared at the start of any energy management/ optimization operation.
* chore: refactor genetic
Refactor the genetic algorithm modules for enhanced module structure and better
readability. Removed unnecessary and overcomplex devices singleton. Also
split devices configuration from genetic algorithm parameters to allow further
development independently from genetic algorithm parameter format. Move
charge rates configuration for electric vehicles from optimization to devices
configuration to allow to have different charge rates for different cars in
the future.
* chore: Rename memory cache to CacheEnergyManagementStore
The name better resembles the task of the cache to chache function and method
results for an energy management run. Also the decorator functions are renamed
accordingly: cachemethod_energy_management, cache_energy_management
* chore: use class properties for config/ems/prediction mixin classes
* chore: skip debug logs from mathplotlib
Mathplotlib is very noisy in debug mode.
* chore: automatically sync bokeh js to bokeh python package
bokeh was updated to 3.8.0, make JS CDN automatically follow the package version.
* chore: rename hello.py to about.py
Make hello.py the adapted EOSdash about page.
* chore: remove demo page from EOSdash
As no the plan and prediction pages are working without configuration, the demo
page is no longer necessary
* chore: split test_server.py for system test
Split test_server.py to create explicit test_system.py for system tests.
* chore: move doc utils to generate_config_md.py
The doc utils are only used in scripts/generate_config_md.py. Move it there to
attribute for strong cohesion.
* chore: improve pydantic merge model documentation
* chore: remove pendulum warning from readme
* chore: remove GitHub discussions from contributing documentation
Github discussions is to be replaced by Akkudoktor.net.
* chore(release): bump version to 0.1.0+dev for development
* build(deps): bump fastapi[standard] from 0.115.14 to 0.117.1
bump fastapi and make coverage version (for pytest-cov) explicit to avoid pip break.
* build(deps): bump uvicorn from 0.36.0 to 0.37.0
BREAKING CHANGE: EOS configuration changed. V1 API changed.
- The available_charge_rates_percent configuration is removed from optimization.
Use the new charge_rate configuration for the electric vehicle
- Optimization configuration parameter hours renamed to horizon_hours
- Device configuration now has to provide the number of devices and device
properties per device.
- Specific prediction provider configuration to be provided by explicit
configuration item (no union for all providers).
- Measurement keys to be provided as a list.
- New feed in tariff providers have to be configured.
- /v1/measurement/loadxxx endpoints are removed. Use generic mesaurement endpoints.
- /v1/admin/cache/clear now clears all cache files. Use
/v1/admin/cache/clear-expired to only clear all expired cache files.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-10-28 02:50:31 +01:00
|
|
|
assert compare_datetimes(provider.ems_start_datetime, sample_forecast_start).equal
|
2024-12-15 14:40:03 +01:00
|
|
|
assert compare_datetimes(provider[0].date_time, to_datetime(sample_forecast_start)).equal
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# Report Generation Test
|
2024-12-30 13:41:39 +01:00
|
|
|
def test_report_ac_power_and_measurement(provider, config_eos):
|
2024-12-15 14:40:03 +01:00
|
|
|
# Set the configuration
|
2024-12-30 13:41:39 +01:00
|
|
|
config_eos.merge_settings_from_dict(sample_config_data)
|
2024-12-15 14:40:03 +01:00
|
|
|
|
|
|
|
|
record = PVForecastAkkudoktorDataRecord(
|
|
|
|
|
pvforecastakkudoktor_ac_power_measured=900.0,
|
|
|
|
|
pvforecast_dc_power=450.0,
|
|
|
|
|
pvforecast_ac_power=400.0,
|
|
|
|
|
)
|
|
|
|
|
provider.append(record)
|
|
|
|
|
|
|
|
|
|
report = provider.report_ac_power_and_measurement()
|
|
|
|
|
assert "DC: 450.0" in report
|
|
|
|
|
assert "AC: 400.0" in report
|
|
|
|
|
assert "AC sampled: 900.0" in report
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
@pytest.mark.skipif(
|
|
|
|
|
sys.platform.startswith("win"), reason="'other_timezone' fixture not supported on Windows"
|
|
|
|
|
)
|
|
|
|
|
@patch("requests.get")
|
|
|
|
|
def test_timezone_behaviour(
|
|
|
|
|
mock_get,
|
|
|
|
|
sample_settings,
|
|
|
|
|
sample_forecast_data_raw,
|
|
|
|
|
sample_forecast_start,
|
|
|
|
|
provider,
|
|
|
|
|
set_other_timezone,
|
|
|
|
|
):
|
|
|
|
|
"""Test PVForecast in another timezone."""
|
|
|
|
|
mock_response = Mock()
|
|
|
|
|
mock_response.status_code = 200
|
|
|
|
|
mock_response.content = sample_forecast_data_raw
|
|
|
|
|
mock_get.return_value = mock_response
|
|
|
|
|
|
|
|
|
|
# sample forecast start in other timezone
|
|
|
|
|
other_timezone = set_other_timezone()
|
|
|
|
|
other_start_datetime = to_datetime(sample_forecast_start, in_timezone=other_timezone)
|
|
|
|
|
assert compare_datetimes(other_start_datetime, sample_forecast_start).equal
|
|
|
|
|
expected_datetime = to_datetime("2024-10-06T00:00:00+0200", in_timezone=other_timezone)
|
|
|
|
|
assert compare_datetimes(other_start_datetime, expected_datetime).equal
|
|
|
|
|
|
|
|
|
|
provider.clear()
|
|
|
|
|
assert len(provider) == 0
|
2024-12-30 13:41:39 +01:00
|
|
|
ems_eos = get_ems()
|
2024-12-15 14:40:03 +01:00
|
|
|
ems_eos.set_start_datetime(other_start_datetime)
|
|
|
|
|
provider.update_data(force_update=True)
|
fix: automatic optimization (#596)
This fix implements the long term goal to have the EOS server run optimization (or
energy management) on regular intervals automatically. Thus clients can request
the current energy management plan at any time and it is updated on regular
intervals without interaction by the client.
This fix started out to "only" make automatic optimization (or energy management)
runs working. It turned out there are several endpoints that in some way
update predictions or run the optimization. To lock against such concurrent attempts
the code had to be refactored to allow control of execution. During refactoring it
became clear that some classes and files are named without a proper reference
to their usage. Thus not only refactoring but also renaming became necessary.
The names are still not the best, but I hope they are more intuitive.
The fix includes several bug fixes that are not directly related to the automatic optimization
but are necessary to keep EOS running properly to do the automatic optimization and
to test and document the changes.
This is a breaking change as the configuration structure changed once again and
the server API was also enhanced and streamlined. The server API that is used by
Andreas and Jörg in their videos has not changed.
* fix: automatic optimization
Allow optimization to automatically run on configured intervals gathering all
optimization parameters from configuration and predictions. The automatic run
can be configured to only run prediction updates skipping the optimization.
Extend documentaion to also cover automatic optimization. Lock automatic runs
against runs initiated by the /optimize or other endpoints. Provide new
endpoints to retrieve the energy management plan and the genetic solution
of the latest automatic optimization run. Offload energy management to thread
pool executor to keep the app more responsive during the CPU heavy optimization
run.
* fix: EOS servers recognize environment variables on startup
Force initialisation of EOS configuration on server startup to assure
all sources of EOS configuration are properly set up and read. Adapt
server tests and configuration tests to also test for environment
variable configuration.
* fix: Remove 0.0.0.0 to localhost translation under Windows
EOS imposed a 0.0.0.0 to localhost translation under Windows for
convenience. This caused some trouble in user configurations. Now, as the
default IP address configuration is 127.0.0.1, the user is responsible
for to set up the correct Windows compliant IP address.
* fix: allow names for hosts additional to IP addresses
* fix: access pydantic model fields by class
Access by instance is deprecated.
* fix: down sampling key_to_array
* fix: make cache clear endpoint clear all cache files
Make /v1/admin/cache/clear clear all cache files. Before it only cleared
expired cache files by default. Add new endpoint /v1/admin/clear-expired
to only clear expired cache files.
* fix: timezonefinder returns Europe/Paris instead of Europe/Berlin
timezonefinder 8.10 got more inaccurate for timezones in europe as there is
a common timezone. Use new package tzfpy instead which is still returning
Europe/Berlin if you are in Germany. tzfpy also claims to be faster than
timezonefinder.
* fix: provider settings configuration
Provider configuration used to be a union holding the settings for several
providers. Pydantic union handling does not always find the correct type
for a provider setting. This led to exceptions in specific configurations.
Now provider settings are explicit comfiguration items for each possible
provider. This is a breaking change as the configuration structure was
changed.
* fix: ClearOutside weather prediction irradiance calculation
Pvlib needs a pandas time index. Convert time index.
* fix: test config file priority
Do not use config_eos fixture as this fixture already creates a config file.
* fix: optimization sample request documentation
Provide all data in documentation of optimization sample request.
* fix: gitlint blocking pip dependency resolution
Replace gitlint by commitizen. Gitlint is not actively maintained anymore.
Gitlint dependencies blocked pip from dependency resolution.
* fix: sync pre-commit config to actual dependency requirements
.pre-commit-config.yaml was out of sync, also requirements-dev.txt.
* fix: missing babel in requirements.txt
Add babel to requirements.txt
* feat: setup default device configuration for automatic optimization
In case the parameters for automatic optimization are not fully defined a
default configuration is setup to allow the automatic energy management
run. The default configuration may help the user to correctly define
the device configuration.
* feat: allow configuration of genetic algorithm parameters
The genetic algorithm parameters for number of individuals, number of
generations, the seed and penalty function parameters are now avaliable
as configuration options.
* feat: allow configuration of home appliance time windows
The time windows a home appliance is allowed to run are now configurable
by the configuration (for /v1 API) and also by the home appliance parameters
(for the classic /optimize API). If there is no such configuration the
time window defaults to optimization hours, which was the standard before
the change. Documentation on how to configure time windows is added.
* feat: standardize mesaurement keys for battery/ ev SoC measurements
The standardized measurement keys to report battery SoC to the device
simulations can now be retrieved from the device configuration as a
read-only config option.
* feat: feed in tariff prediction
Add feed in tarif predictions needed for automatic optimization. The feed in
tariff can be retrieved as fixed feed in tarif or can be imported. Also add
tests for the different feed in tariff providers. Extend documentation to
cover the feed in tariff providers.
* feat: add energy management plan based on S2 standard instructions
EOS can generate an energy management plan as a list of simple instructions.
May be retrieved by the /v1/energy-management/plan endpoint. The instructions
loosely follow the S2 energy management standard.
* feat: make measurement keys configurable by EOS configuration.
The fixed measurement keys are replaced by configurable measurement keys.
* feat: make pendulum DateTime, Date, Duration types usable for pydantic models
Use pydantic_extra_types.pendulum_dt to get pydantic pendulum types. Types are
added to the datetimeutil utility. Remove custom made pendulum adaptations
from EOS pydantic module. Make EOS modules use the pydantic pendulum types
managed by the datetimeutil module instead of the core pendulum types.
* feat: Add Time, TimeWindow, TimeWindowSequence and to_time to datetimeutil.
The time windows are are added to support home appliance time window
configuration. All time classes are also pydantic models. Time is the base
class for time definition derived from pendulum.Time.
* feat: Extend DataRecord by configurable field like data.
Configurable field like data was added to support the configuration of
measurement records.
* feat: Add additional information to health information
Version information is added to the health endpoints of eos and eosDash.
The start time of the last optimization and the latest run time of the energy
management is added to the EOS health information.
* feat: add pydantic merge model tests
* feat: add plan tab to EOSdash
The plan tab displays the current energy management instructions.
* feat: add predictions tab to EOSdash
The predictions tab displays the current predictions.
* feat: add cache management to EOSdash admin tab
The admin tab is extended by a section for cache management. It allows to
clear the cache.
* feat: add about tab to EOSdash
The about tab resembles the former hello tab and provides extra information.
* feat: Adapt changelog and prepare for release management
Release management using commitizen is added. The changelog file is adapted and
teh changelog and a description for release management is added in the
documentation.
* feat(doc): Improve install and devlopment documentation
Provide a more concise installation description in Readme.md and add extra
installation page and development page to documentation.
* chore: Use memory cache for interpolation instead of dict in inverter
Decorate calculate_self_consumption() with @cachemethod_until_update to cache
results in memory during an energy management/ optimization run. Replacement
of dict type caching in inverter is now possible because all optimization
runs are properly locked and the memory cache CacheUntilUpdateStore is properly
cleared at the start of any energy management/ optimization operation.
* chore: refactor genetic
Refactor the genetic algorithm modules for enhanced module structure and better
readability. Removed unnecessary and overcomplex devices singleton. Also
split devices configuration from genetic algorithm parameters to allow further
development independently from genetic algorithm parameter format. Move
charge rates configuration for electric vehicles from optimization to devices
configuration to allow to have different charge rates for different cars in
the future.
* chore: Rename memory cache to CacheEnergyManagementStore
The name better resembles the task of the cache to chache function and method
results for an energy management run. Also the decorator functions are renamed
accordingly: cachemethod_energy_management, cache_energy_management
* chore: use class properties for config/ems/prediction mixin classes
* chore: skip debug logs from mathplotlib
Mathplotlib is very noisy in debug mode.
* chore: automatically sync bokeh js to bokeh python package
bokeh was updated to 3.8.0, make JS CDN automatically follow the package version.
* chore: rename hello.py to about.py
Make hello.py the adapted EOSdash about page.
* chore: remove demo page from EOSdash
As no the plan and prediction pages are working without configuration, the demo
page is no longer necessary
* chore: split test_server.py for system test
Split test_server.py to create explicit test_system.py for system tests.
* chore: move doc utils to generate_config_md.py
The doc utils are only used in scripts/generate_config_md.py. Move it there to
attribute for strong cohesion.
* chore: improve pydantic merge model documentation
* chore: remove pendulum warning from readme
* chore: remove GitHub discussions from contributing documentation
Github discussions is to be replaced by Akkudoktor.net.
* chore(release): bump version to 0.1.0+dev for development
* build(deps): bump fastapi[standard] from 0.115.14 to 0.117.1
bump fastapi and make coverage version (for pytest-cov) explicit to avoid pip break.
* build(deps): bump uvicorn from 0.36.0 to 0.37.0
BREAKING CHANGE: EOS configuration changed. V1 API changed.
- The available_charge_rates_percent configuration is removed from optimization.
Use the new charge_rate configuration for the electric vehicle
- Optimization configuration parameter hours renamed to horizon_hours
- Device configuration now has to provide the number of devices and device
properties per device.
- Specific prediction provider configuration to be provided by explicit
configuration item (no union for all providers).
- Measurement keys to be provided as a list.
- New feed in tariff providers have to be configured.
- /v1/measurement/loadxxx endpoints are removed. Use generic mesaurement endpoints.
- /v1/admin/cache/clear now clears all cache files. Use
/v1/admin/cache/clear-expired to only clear all expired cache files.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2025-10-28 02:50:31 +01:00
|
|
|
assert compare_datetimes(provider.ems_start_datetime, other_start_datetime).equal
|
2024-12-15 14:40:03 +01:00
|
|
|
# Check wether first record starts at requested sample start time
|
|
|
|
|
assert compare_datetimes(provider[0].date_time, sample_forecast_start).equal
|
|
|
|
|
|
|
|
|
|
# Test updating AC power measurement for a specific date.
|
|
|
|
|
provider.update_value(sample_forecast_start, "pvforecastakkudoktor_ac_power_measured", 1000)
|
|
|
|
|
# Check wether first record was filled with ac power measurement
|
|
|
|
|
assert provider[0].pvforecastakkudoktor_ac_power_measured == 1000
|
|
|
|
|
|
|
|
|
|
# Test fetching temperature forecast for a specific date.
|
|
|
|
|
other_end_datetime = other_start_datetime + to_duration("24 hours")
|
|
|
|
|
expected_end_datetime = to_datetime("2024-10-07T00:00:00+0200", in_timezone=other_timezone)
|
|
|
|
|
assert compare_datetimes(other_end_datetime, expected_end_datetime).equal
|
|
|
|
|
forecast_temps = provider.key_to_series(
|
|
|
|
|
"pvforecastakkudoktor_temp_air", other_start_datetime, other_end_datetime
|
|
|
|
|
)
|
2024-12-29 16:43:37 +01:00
|
|
|
assert len(forecast_temps) == 23 # 24-1, first temperature is null
|
|
|
|
|
assert forecast_temps.iloc[0] == 6.5
|
|
|
|
|
assert forecast_temps.iloc[1] == 6.0
|
2024-12-15 14:40:03 +01:00
|
|
|
|
|
|
|
|
# Test fetching AC power forecast
|
|
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other_end_datetime = other_start_datetime + to_duration("48 hours")
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forecast_measured = provider.key_to_series(
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"pvforecastakkudoktor_ac_power_measured", other_start_datetime, other_end_datetime
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)
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2024-12-29 18:42:49 +01:00
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assert len(forecast_measured) == 1
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2024-12-15 14:40:03 +01:00
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assert forecast_measured.iloc[0] == 1000.0 # changed before
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